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    An AI recipe recommendation server based on the MCP protocol, providing functions such as recipe query, classification filtering, intelligent dietary planning, and daily menu recommendation.
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    1
    Apache 2.0
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    Provides local MCP tools to query your own Xiaomi Mi Fitness health data (sleep, workouts, heart rate, etc.) stored in a local SQLite database, enabling AI assistants to access personal health records without exposing credentials.
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    AGPL 3.0
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    MCP server that lets you ask questions about your Garmin training history in plain language. It ingests FIT files into DuckDB locally and exposes typed tools for analyzing activities, streams, and summaries.
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    MIT
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    Ask your Oura Ring about sleep, readiness, activity, stress and heart rate in ChatGPT or Claude, in any language. Read-only, 10 task-oriented tools; runs in a sandbox demo mode with no credentials.
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    20 npm
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    MIT
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    Enables AI assistants to answer food safety and shelf-life questions using USDA-grade data, with tools for shelf life, safety checks, storage tips, and waste cost calculations.
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    36 npm
    MIT
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    Local MCP server to access your personal health data from E-Nabız (Turkish Ministry of Health) via an LLM. Read-only, secure, and respects privacy.
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    MIT
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    SuperGlookoQuery is a Claude Desktop extension (MCPB) that connects to Glooko diabetes device data and exposes it as clinical analysis tools (time in range, GMI, trends, enriched bolus/basal analysis, charting), for any pump/CGM combination Glooko supports rather than one fixed device. It's a fork of Richard Hall's podquery-mcp, reworked to discover each account's own data shape rather than assumi
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    MIT
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    Provides Claude Code and Claude Desktop with safe, read-only access to your local Garmin training data, readiness decisions, run details, and trends so you can get coaching advice in natural language.
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    MIT
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    Provides SQL access to the ANSES Ciqual French food composition database with nutritional data for over 3,000 foods. Supports full-text search and bilingual queries for comprehensive nutrition analysis.
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    MIT
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    Enables AI agents to read and analyze your Boardsesh climbing logbook across boards like Kilter, Tension, and MoonBoard, with read-only access and optional credentials for personalized recommendations.
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    MIT
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    A stateless MCP server that normalizes strength training logs into a deterministic canonical schema, enabling consumers to build analytics downstream without storing data server-side.
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    Provides read-only access to your Garmin Connect health data, including sleep, HRV, body battery, stress, training readiness, and activities, through an MCP server.
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    Enables AI agents to securely access Apple Health data (sleep, heart rate, menstrual cycle, etc.) via end-to-end encrypted local decryption from the Tether iOS app.
    26
    407 PyPI
    2
    MIT